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<pre><span class="sourceLineNo">001</span>/*-<a name="line.1"></a>
<span class="sourceLineNo">002</span> * Copyright 2016 Diamond Light Source Ltd.<a name="line.2"></a>
<span class="sourceLineNo">003</span> *<a name="line.3"></a>
<span class="sourceLineNo">004</span> * All rights reserved. This program and the accompanying materials<a name="line.4"></a>
<span class="sourceLineNo">005</span> * are made available under the terms of the Eclipse Public License v1.0<a name="line.5"></a>
<span class="sourceLineNo">006</span> * which accompanies this distribution, and is available at<a name="line.6"></a>
<span class="sourceLineNo">007</span> * http://www.eclipse.org/legal/epl-v10.html<a name="line.7"></a>
<span class="sourceLineNo">008</span> */<a name="line.8"></a>
<span class="sourceLineNo">009</span><a name="line.9"></a>
<span class="sourceLineNo">010</span>package org.eclipse.january.dataset;<a name="line.10"></a>
<span class="sourceLineNo">011</span><a name="line.11"></a>
<span class="sourceLineNo">012</span>/**<a name="line.12"></a>
<span class="sourceLineNo">013</span> * Estimators of the scale of a Dataset.<a name="line.13"></a>
<span class="sourceLineNo">014</span> * &lt;p&gt;<a name="line.14"></a>
<span class="sourceLineNo">015</span> * A class of static methods to produce estimations of the scale of variation within a Dataset.<a name="line.15"></a>
<span class="sourceLineNo">016</span> * The available estimators are:<a name="line.16"></a>
<span class="sourceLineNo">017</span> * &lt;ul&gt;<a name="line.17"></a>
<span class="sourceLineNo">018</span> * &lt;li&gt; Median Absolute Deviation &lt;/li&gt;<a name="line.18"></a>
<span class="sourceLineNo">019</span> * &lt;li&gt; S&lt;sub&gt;n&lt;/sub&gt; of Croux and Rousseeuw (1992).&lt;/li&gt;<a name="line.19"></a>
<span class="sourceLineNo">020</span> * &lt;/ul&gt; <a name="line.20"></a>
<span class="sourceLineNo">021</span> * &lt;p&gt;<a name="line.21"></a>
<span class="sourceLineNo">022</span> * Croux, C. and P. J. Rousseeuw, "Time-efficient algorithms for two highly robust estimators of scale", Computational Statistics, Volume 1, eds. Y. Dodge and J.Whittaker, Physica-Verlag, Heidelberg, pp411--428 (1992).<a name="line.22"></a>
<span class="sourceLineNo">023</span> */<a name="line.23"></a>
<span class="sourceLineNo">024</span>public class Outliers {<a name="line.24"></a>
<span class="sourceLineNo">025</span><a name="line.25"></a>
<span class="sourceLineNo">026</span>        private final static double MADSCALEFACTOR = 1.4826;<a name="line.26"></a>
<span class="sourceLineNo">027</span>        private final static double SNSCALEFACTOR = 1.1926;<a name="line.27"></a>
<span class="sourceLineNo">028</span>        <a name="line.28"></a>
<span class="sourceLineNo">029</span>        /**<a name="line.29"></a>
<span class="sourceLineNo">030</span>         * Returns the Median Absolute Deviation (MAD) and the median. <a name="line.30"></a>
<span class="sourceLineNo">031</span>         * @param data<a name="line.31"></a>
<span class="sourceLineNo">032</span>         *                      The data for which the median and the MAD are to be calculated<a name="line.32"></a>
<span class="sourceLineNo">033</span>         * @return A two-element array of doubles, consisting of the MAD and the median of the data<a name="line.33"></a>
<span class="sourceLineNo">034</span>         */<a name="line.34"></a>
<span class="sourceLineNo">035</span>        public static double[] medianAbsoluteDeviation(Dataset data) {<a name="line.35"></a>
<span class="sourceLineNo">036</span>                <a name="line.36"></a>
<span class="sourceLineNo">037</span>                double median = (Double)Stats.median(data);<a name="line.37"></a>
<span class="sourceLineNo">038</span>                data = Maths.subtract(data, median);<a name="line.38"></a>
<span class="sourceLineNo">039</span>                data = Maths.abs(data);<a name="line.39"></a>
<span class="sourceLineNo">040</span>                double median2 = (Double)Stats.median(data);<a name="line.40"></a>
<span class="sourceLineNo">041</span>                double mad = MADSCALEFACTOR * median2;<a name="line.41"></a>
<span class="sourceLineNo">042</span>                <a name="line.42"></a>
<span class="sourceLineNo">043</span>                return new double[]{mad, median};<a name="line.43"></a>
<span class="sourceLineNo">044</span>        }<a name="line.44"></a>
<span class="sourceLineNo">045</span>        <a name="line.45"></a>
<span class="sourceLineNo">046</span>        /**<a name="line.46"></a>
<span class="sourceLineNo">047</span>         * Returns the Sn estimator of Croux and Rousseeuw.<a name="line.47"></a>
<span class="sourceLineNo">048</span>         * &lt;p&gt;<a name="line.48"></a>
<span class="sourceLineNo">049</span>         * This is the simple O(n²) version of the calculation algorithm.<a name="line.49"></a>
<span class="sourceLineNo">050</span>         * @param data<a name="line.50"></a>
<span class="sourceLineNo">051</span>         *                      The data for which the estimator is to be calculated.<a name="line.51"></a>
<span class="sourceLineNo">052</span>         * @return The value of the Sn estimator for the data<a name="line.52"></a>
<span class="sourceLineNo">053</span>         */<a name="line.53"></a>
<span class="sourceLineNo">054</span>        public static double snNaive(Dataset data) {<a name="line.54"></a>
<span class="sourceLineNo">055</span>                <a name="line.55"></a>
<span class="sourceLineNo">056</span>                Dataset medAbs = DatasetFactory.zeros(data);<a name="line.56"></a>
<span class="sourceLineNo">057</span>                Dataset dif = DatasetFactory.zeros(data);<a name="line.57"></a>
<span class="sourceLineNo">058</span>                <a name="line.58"></a>
<span class="sourceLineNo">059</span>                IndexIterator it = data.getIterator();<a name="line.59"></a>
<span class="sourceLineNo">060</span>                int count = 0;<a name="line.60"></a>
<span class="sourceLineNo">061</span>                while (it.hasNext()) {<a name="line.61"></a>
<span class="sourceLineNo">062</span>                        double val = data.getElementDoubleAbs(it.index);<a name="line.62"></a>
<span class="sourceLineNo">063</span>                        Maths.subtract(data, val, dif);<a name="line.63"></a>
<span class="sourceLineNo">064</span>                        Maths.abs(dif,dif);<a name="line.64"></a>
<span class="sourceLineNo">065</span>                        //Lower median - Math.floor((n/2)+1) of sorted<a name="line.65"></a>
<span class="sourceLineNo">066</span>                        dif.sort(null);<a name="line.66"></a>
<span class="sourceLineNo">067</span>                        medAbs.setObjectAbs(count++, lowMed(dif));<a name="line.67"></a>
<span class="sourceLineNo">068</span>                }<a name="line.68"></a>
<span class="sourceLineNo">069</span>                <a name="line.69"></a>
<span class="sourceLineNo">070</span>                <a name="line.70"></a>
<span class="sourceLineNo">071</span>                //Higher median - Math.floor((n+1)/2) of sorted<a name="line.71"></a>
<span class="sourceLineNo">072</span>                medAbs.sort(null);<a name="line.72"></a>
<span class="sourceLineNo">073</span>                double median = highMed(medAbs);<a name="line.73"></a>
<span class="sourceLineNo">074</span>                <a name="line.74"></a>
<span class="sourceLineNo">075</span>                return median * SNSCALEFACTOR;<a name="line.75"></a>
<span class="sourceLineNo">076</span>        }<a name="line.76"></a>
<span class="sourceLineNo">077</span>        <a name="line.77"></a>
<span class="sourceLineNo">078</span>        /**<a name="line.78"></a>
<span class="sourceLineNo">079</span>         * Returns the Sn estimator of Croux and Rousseeuw.<a name="line.79"></a>
<span class="sourceLineNo">080</span>         * &lt;p&gt;<a name="line.80"></a>
<span class="sourceLineNo">081</span>         * This is the complex O(nlog n) version of the calculation algorithm.<a name="line.81"></a>
<span class="sourceLineNo">082</span>         * @param data<a name="line.82"></a>
<span class="sourceLineNo">083</span>         *                      The data for which the estimator is to be calculated.<a name="line.83"></a>
<span class="sourceLineNo">084</span>         * @return The value of the Sn estimator for the data<a name="line.84"></a>
<span class="sourceLineNo">085</span>         */<a name="line.85"></a>
<span class="sourceLineNo">086</span>        public static double snFast(Dataset data) {<a name="line.86"></a>
<span class="sourceLineNo">087</span>                <a name="line.87"></a>
<span class="sourceLineNo">088</span>                Dataset sorted = data.clone();<a name="line.88"></a>
<span class="sourceLineNo">089</span>                sorted.sort(null);<a name="line.89"></a>
<span class="sourceLineNo">090</span>                <a name="line.90"></a>
<span class="sourceLineNo">091</span>                Dataset medAbs = DatasetFactory.zeros(data);<a name="line.91"></a>
<span class="sourceLineNo">092</span>                <a name="line.92"></a>
<span class="sourceLineNo">093</span>                IndexIterator it = data.getIterator();<a name="line.93"></a>
<span class="sourceLineNo">094</span>                int count = 0;<a name="line.94"></a>
<span class="sourceLineNo">095</span>                while (it.hasNext()) {<a name="line.95"></a>
<span class="sourceLineNo">096</span>                        MedianOfTwoSortedSets snuff = new MedianForSn(sorted, it.index);<a name="line.96"></a>
<span class="sourceLineNo">097</span>                        medAbs.setObjectAbs(count++, snuff.get());<a name="line.97"></a>
<span class="sourceLineNo">098</span>                }<a name="line.98"></a>
<span class="sourceLineNo">099</span>                <a name="line.99"></a>
<span class="sourceLineNo">100</span>                <a name="line.100"></a>
<span class="sourceLineNo">101</span>                //Higher median - Math.floor((n+1)/2) of sorted<a name="line.101"></a>
<span class="sourceLineNo">102</span>                medAbs.sort(null);<a name="line.102"></a>
<span class="sourceLineNo">103</span>                double median = highMed(medAbs);<a name="line.103"></a>
<span class="sourceLineNo">104</span>                <a name="line.104"></a>
<span class="sourceLineNo">105</span>                return median * SNSCALEFACTOR;<a name="line.105"></a>
<span class="sourceLineNo">106</span>        }<a name="line.106"></a>
<span class="sourceLineNo">107</span>        <a name="line.107"></a>
<span class="sourceLineNo">108</span>        /**<a name="line.108"></a>
<span class="sourceLineNo">109</span>         * Returns the lomed<a name="line.109"></a>
<span class="sourceLineNo">110</span>         * &lt;p&gt;<a name="line.110"></a>
<span class="sourceLineNo">111</span>         * Returns the lomed (low median) of a sorted Dataset.<a name="line.111"></a>
<span class="sourceLineNo">112</span>         * @param data<a name="line.112"></a>
<span class="sourceLineNo">113</span>         *                      A sorted Dataset for which the low median is to be calculated.  <a name="line.113"></a>
<span class="sourceLineNo">114</span>         * @return<a name="line.114"></a>
<span class="sourceLineNo">115</span>         *              The value of the lomed of the data<a name="line.115"></a>
<span class="sourceLineNo">116</span>         */<a name="line.116"></a>
<span class="sourceLineNo">117</span>        public static double lowMed(Dataset data) {<a name="line.117"></a>
<span class="sourceLineNo">118</span>                return data.getElementDoubleAbs((int)Math.floor((data.getSize()/2)));<a name="line.118"></a>
<span class="sourceLineNo">119</span>        }<a name="line.119"></a>
<span class="sourceLineNo">120</span>        <a name="line.120"></a>
<span class="sourceLineNo">121</span>        /**<a name="line.121"></a>
<span class="sourceLineNo">122</span>         * Returns the himed<a name="line.122"></a>
<span class="sourceLineNo">123</span>         * &lt;p&gt;<a name="line.123"></a>
<span class="sourceLineNo">124</span>         * Returns the himed (high median) of a sorted Dataset.<a name="line.124"></a>
<span class="sourceLineNo">125</span>         * @param data<a name="line.125"></a>
<span class="sourceLineNo">126</span>         *                      A sorted Dataset for which the low median is to be calculated.  <a name="line.126"></a>
<span class="sourceLineNo">127</span>         * @return<a name="line.127"></a>
<span class="sourceLineNo">128</span>         *              The value of the himed of the data<a name="line.128"></a>
<span class="sourceLineNo">129</span>         */<a name="line.129"></a>
<span class="sourceLineNo">130</span>        public static double highMed(Dataset data) {<a name="line.130"></a>
<span class="sourceLineNo">131</span>                return data.getElementDoubleAbs((int)Math.floor((data.getSize()+1)/2-1));<a name="line.131"></a>
<span class="sourceLineNo">132</span>        }<a name="line.132"></a>
<span class="sourceLineNo">133</span><a name="line.133"></a>
<span class="sourceLineNo">134</span>        /**<a name="line.134"></a>
<span class="sourceLineNo">135</span>         * Calculates the overall median of two double arrays<a name="line.135"></a>
<span class="sourceLineNo">136</span>         * @param a<a name="line.136"></a>
<span class="sourceLineNo">137</span>         * @param b<a name="line.137"></a>
<span class="sourceLineNo">138</span>         *                      the two arrays for which the overall median is desired. <a name="line.138"></a>
<span class="sourceLineNo">139</span>         * @return the overall median of the two arrays<a name="line.139"></a>
<span class="sourceLineNo">140</span>         */<a name="line.140"></a>
<span class="sourceLineNo">141</span>        public static double medianOFTwoPrimitiveArrays (double[] a, double[] b) {<a name="line.141"></a>
<span class="sourceLineNo">142</span>                MedianOfTwoArrays medio = new MedianOfTwoArrays(a, b);<a name="line.142"></a>
<span class="sourceLineNo">143</span>                return medio.get();<a name="line.143"></a>
<span class="sourceLineNo">144</span>        }<a name="line.144"></a>
<span class="sourceLineNo">145</span>}<a name="line.145"></a>
<span class="sourceLineNo">146</span><a name="line.146"></a>
<span class="sourceLineNo">147</span>/**<a name="line.147"></a>
<span class="sourceLineNo">148</span> * Allows the calculation of the median of two arrays.<a name="line.148"></a>
<span class="sourceLineNo">149</span> * &lt;p&gt;<a name="line.149"></a>
<span class="sourceLineNo">150</span> * Subclasses must implement getA() and getB() to return the elements of A or B<a name="line.150"></a>
<span class="sourceLineNo">151</span> * at the given index. The length of a must be less than or equal to that of b.<a name="line.151"></a>
<span class="sourceLineNo">152</span> * The constructor must set the sizes nA and nB.<a name="line.152"></a>
<span class="sourceLineNo">153</span> */<a name="line.153"></a>
<span class="sourceLineNo">154</span>abstract class MedianOfTwoSortedSets{<a name="line.154"></a>
<span class="sourceLineNo">155</span>        int nB, nA, diff, diffLeft;<a name="line.155"></a>
<span class="sourceLineNo">156</span>        <a name="line.156"></a>
<span class="sourceLineNo">157</span>        public final double get() {<a name="line.157"></a>
<span class="sourceLineNo">158</span>                // Initialize the left and right markers for the set of candidate <a name="line.158"></a>
<span class="sourceLineNo">159</span>                // values. These are inclusive on both left and right.<a name="line.159"></a>
<span class="sourceLineNo">160</span>                int leftA = 0, leftB = 0;<a name="line.160"></a>
<span class="sourceLineNo">161</span>                @SuppressWarnings("unused") // keep rightA for symmetry<a name="line.161"></a>
<span class="sourceLineNo">162</span>                int rightA = nB-1, rightB = nB-1;<a name="line.162"></a>
<span class="sourceLineNo">163</span>                <a name="line.163"></a>
<span class="sourceLineNo">164</span>                while (nB &gt; 1) {<a name="line.164"></a>
<span class="sourceLineNo">165</span>                        // For 0-based indexing, the lomed is the element at floor((n+1)/2)-1<a name="line.165"></a>
<span class="sourceLineNo">166</span>                        int medianIndex = (int) Math.floor((nB+1)/2)-1;<a name="line.166"></a>
<span class="sourceLineNo">167</span>                        int medianAIndex = leftA + medianIndex,<a name="line.167"></a>
<span class="sourceLineNo">168</span>                                        medianBIndex = leftB + medianIndex;<a name="line.168"></a>
<span class="sourceLineNo">169</span>                        double medA = getAm(medianAIndex),<a name="line.169"></a>
<span class="sourceLineNo">170</span>                                        medB = getBm(medianBIndex);<a name="line.170"></a>
<span class="sourceLineNo">171</span><a name="line.171"></a>
<span class="sourceLineNo">172</span>                        int smallerShift = 0;<a name="line.172"></a>
<span class="sourceLineNo">173</span>                        if (nB % 2 == 0) {<a name="line.173"></a>
<span class="sourceLineNo">174</span>                                // N even: the smaller lomed, as well as anything smaller than it, cannot be the overall median<a name="line.174"></a>
<span class="sourceLineNo">175</span>                                smallerShift = +1;<a name="line.175"></a>
<span class="sourceLineNo">176</span>                        }<a name="line.176"></a>
<span class="sourceLineNo">177</span>                        <a name="line.177"></a>
<span class="sourceLineNo">178</span>                        if (medA &gt;= medB) {<a name="line.178"></a>
<span class="sourceLineNo">179</span>                                rightA = medianAIndex;<a name="line.179"></a>
<span class="sourceLineNo">180</span>                                leftB = medianBIndex + smallerShift;<a name="line.180"></a>
<span class="sourceLineNo">181</span>                        } else {<a name="line.181"></a>
<span class="sourceLineNo">182</span>                                rightB = medianBIndex;<a name="line.182"></a>
<span class="sourceLineNo">183</span>                                leftA = medianAIndex + smallerShift;<a name="line.183"></a>
<span class="sourceLineNo">184</span>                        }<a name="line.184"></a>
<span class="sourceLineNo">185</span>                        <a name="line.185"></a>
<span class="sourceLineNo">186</span>                        // Different lengths<a name="line.186"></a>
<span class="sourceLineNo">187</span>                        // It should be floor((l_m-1 + 1)/2))<a name="line.187"></a>
<span class="sourceLineNo">188</span>                        // this is newLength, defined above<a name="line.188"></a>
<span class="sourceLineNo">189</span>                        // Difference between left and right<a name="line.189"></a>
<span class="sourceLineNo">190</span>                        nB = rightB - leftB + 1;<a name="line.190"></a>
<span class="sourceLineNo">191</span>                }<a name="line.191"></a>
<span class="sourceLineNo">192</span><a name="line.192"></a>
<span class="sourceLineNo">193</span>                // when the array is length 1, right and left will be the same.<a name="line.193"></a>
<span class="sourceLineNo">194</span>                // The lomed of a two element array is the smaller of the two<a name="line.194"></a>
<span class="sourceLineNo">195</span>                return Math.min(getAm(leftA), getBm(leftB));<a name="line.195"></a>
<span class="sourceLineNo">196</span>        }<a name="line.196"></a>
<span class="sourceLineNo">197</span>        <a name="line.197"></a>
<span class="sourceLineNo">198</span>        // Get the value in the expanded array<a name="line.198"></a>
<span class="sourceLineNo">199</span>        private double getAm(int i) {<a name="line.199"></a>
<span class="sourceLineNo">200</span>                int firstElement = diffLeft,<a name="line.200"></a>
<span class="sourceLineNo">201</span>                                lastElement = diffLeft + nA - 1;<a name="line.201"></a>
<span class="sourceLineNo">202</span>                if (i &lt; firstElement) {<a name="line.202"></a>
<span class="sourceLineNo">203</span>                        return Double.NEGATIVE_INFINITY;<a name="line.203"></a>
<span class="sourceLineNo">204</span>                } else if (i &gt; lastElement) {<a name="line.204"></a>
<span class="sourceLineNo">205</span>                        return Double.POSITIVE_INFINITY;<a name="line.205"></a>
<span class="sourceLineNo">206</span>                } else {<a name="line.206"></a>
<span class="sourceLineNo">207</span>                        return getA(i - diffLeft);<a name="line.207"></a>
<span class="sourceLineNo">208</span>                }<a name="line.208"></a>
<span class="sourceLineNo">209</span>        }<a name="line.209"></a>
<span class="sourceLineNo">210</span>        <a name="line.210"></a>
<span class="sourceLineNo">211</span>        private double getBm(int i) {<a name="line.211"></a>
<span class="sourceLineNo">212</span>                return getB(i);<a name="line.212"></a>
<span class="sourceLineNo">213</span>        }<a name="line.213"></a>
<span class="sourceLineNo">214</span><a name="line.214"></a>
<span class="sourceLineNo">215</span>        // Get the values in the original arrays<a name="line.215"></a>
<span class="sourceLineNo">216</span>        protected abstract double getA(int i);  <a name="line.216"></a>
<span class="sourceLineNo">217</span>        protected abstract double getB(int i);<a name="line.217"></a>
<span class="sourceLineNo">218</span>        <a name="line.218"></a>
<span class="sourceLineNo">219</span>        // Call this to set up the length difference variables. <a name="line.219"></a>
<span class="sourceLineNo">220</span>        protected void setDiffs() {<a name="line.220"></a>
<span class="sourceLineNo">221</span>                diff = nB - nA;<a name="line.221"></a>
<span class="sourceLineNo">222</span>                diffLeft = diff/2;<a name="line.222"></a>
<span class="sourceLineNo">223</span>        }<a name="line.223"></a>
<span class="sourceLineNo">224</span>}<a name="line.224"></a>
<span class="sourceLineNo">225</span><a name="line.225"></a>
<span class="sourceLineNo">226</span>class MedianOfTwoArrays extends MedianOfTwoSortedSets {<a name="line.226"></a>
<span class="sourceLineNo">227</span><a name="line.227"></a>
<span class="sourceLineNo">228</span>        double[] a, b;<a name="line.228"></a>
<span class="sourceLineNo">229</span><a name="line.229"></a>
<span class="sourceLineNo">230</span>        public MedianOfTwoArrays(double[] ain, double[] bin) {<a name="line.230"></a>
<span class="sourceLineNo">231</span>                if (bin.length &gt;= ain.length) {<a name="line.231"></a>
<span class="sourceLineNo">232</span>                        this.a = ain;<a name="line.232"></a>
<span class="sourceLineNo">233</span>                        nA = ain.length;<a name="line.233"></a>
<span class="sourceLineNo">234</span>                        this.b = bin;<a name="line.234"></a>
<span class="sourceLineNo">235</span>                        nB = bin.length;<a name="line.235"></a>
<span class="sourceLineNo">236</span>                } else {<a name="line.236"></a>
<span class="sourceLineNo">237</span>                        this.a = bin;<a name="line.237"></a>
<span class="sourceLineNo">238</span>                        nA = bin.length;<a name="line.238"></a>
<span class="sourceLineNo">239</span>                        this.b = ain;<a name="line.239"></a>
<span class="sourceLineNo">240</span>                        nB = ain.length;<a name="line.240"></a>
<span class="sourceLineNo">241</span>                }<a name="line.241"></a>
<span class="sourceLineNo">242</span>                setDiffs();<a name="line.242"></a>
<span class="sourceLineNo">243</span>        }<a name="line.243"></a>
<span class="sourceLineNo">244</span>        @Override<a name="line.244"></a>
<span class="sourceLineNo">245</span>        protected double getA(int i) {<a name="line.245"></a>
<span class="sourceLineNo">246</span>                return a[i];<a name="line.246"></a>
<span class="sourceLineNo">247</span>        }<a name="line.247"></a>
<span class="sourceLineNo">248</span>        @Override<a name="line.248"></a>
<span class="sourceLineNo">249</span>        protected double getB(int i) {<a name="line.249"></a>
<span class="sourceLineNo">250</span>                return b[i];<a name="line.250"></a>
<span class="sourceLineNo">251</span>        }<a name="line.251"></a>
<span class="sourceLineNo">252</span>}<a name="line.252"></a>
<span class="sourceLineNo">253</span><a name="line.253"></a>
<span class="sourceLineNo">254</span>class MedianForSn extends MedianOfTwoSortedSets {<a name="line.254"></a>
<span class="sourceLineNo">255</span>        Dataset xj;<a name="line.255"></a>
<span class="sourceLineNo">256</span>        int referenceIndex;<a name="line.256"></a>
<span class="sourceLineNo">257</span>        boolean lowerIsBigger;<a name="line.257"></a>
<span class="sourceLineNo">258</span><a name="line.258"></a>
<span class="sourceLineNo">259</span>        public MedianForSn(Dataset xj, int referenceIndex) {<a name="line.259"></a>
<span class="sourceLineNo">260</span>                this.xj = xj;<a name="line.260"></a>
<span class="sourceLineNo">261</span>                this.referenceIndex = referenceIndex;<a name="line.261"></a>
<span class="sourceLineNo">262</span><a name="line.262"></a>
<span class="sourceLineNo">263</span>                // determine which of the two halves of the array is larger<a name="line.263"></a>
<span class="sourceLineNo">264</span>                int lowerSize = referenceIndex, upperSize = xj.getSize() - referenceIndex - 1;<a name="line.264"></a>
<span class="sourceLineNo">265</span>                lowerIsBigger = lowerSize &gt; upperSize;<a name="line.265"></a>
<span class="sourceLineNo">266</span><a name="line.266"></a>
<span class="sourceLineNo">267</span>                // Set the array sizes<a name="line.267"></a>
<span class="sourceLineNo">268</span>                if (lowerIsBigger) {<a name="line.268"></a>
<span class="sourceLineNo">269</span>                        nA = upperSize;<a name="line.269"></a>
<span class="sourceLineNo">270</span>                        nB = lowerSize;<a name="line.270"></a>
<span class="sourceLineNo">271</span>                } else {<a name="line.271"></a>
<span class="sourceLineNo">272</span>                        nA = lowerSize;<a name="line.272"></a>
<span class="sourceLineNo">273</span>                        nB = upperSize;<a name="line.273"></a>
<span class="sourceLineNo">274</span>                }<a name="line.274"></a>
<span class="sourceLineNo">275</span>                setDiffs();<a name="line.275"></a>
<span class="sourceLineNo">276</span>        }<a name="line.276"></a>
<span class="sourceLineNo">277</span><a name="line.277"></a>
<span class="sourceLineNo">278</span>        @Override<a name="line.278"></a>
<span class="sourceLineNo">279</span>        protected double getA(int i) {<a name="line.279"></a>
<span class="sourceLineNo">280</span>                return (!lowerIsBigger) ? getLower(i) : getUpper(i);<a name="line.280"></a>
<span class="sourceLineNo">281</span>        }<a name="line.281"></a>
<span class="sourceLineNo">282</span>        @Override<a name="line.282"></a>
<span class="sourceLineNo">283</span>        protected double getB(int i) {<a name="line.283"></a>
<span class="sourceLineNo">284</span>                return (!lowerIsBigger) ? getUpper(i) : getLower(i);<a name="line.284"></a>
<span class="sourceLineNo">285</span>        }<a name="line.285"></a>
<span class="sourceLineNo">286</span>        <a name="line.286"></a>
<span class="sourceLineNo">287</span>        private double getLower(int i) {<a name="line.287"></a>
<span class="sourceLineNo">288</span>                return xj.getDouble(referenceIndex) - xj.getDouble(referenceIndex - 1 - i);<a name="line.288"></a>
<span class="sourceLineNo">289</span>        }<a name="line.289"></a>
<span class="sourceLineNo">290</span>        private double getUpper(int i) {<a name="line.290"></a>
<span class="sourceLineNo">291</span>                return xj.getDouble(i + referenceIndex + 1) - xj.getDouble(referenceIndex);<a name="line.291"></a>
<span class="sourceLineNo">292</span>        }<a name="line.292"></a>
<span class="sourceLineNo">293</span>}<a name="line.293"></a>




























































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